A linear oscillator model predicts dynamic temporal attention and pupillary entrainment to rhythmic patterns by Lauren K. Fink, Brian K. Hurley, Joy J. Geng, Petr Janata

“…Rhythm is a ubiquitous feature of music that induces specific neural modes of processing. …”
Get full text

AI-Powered Vocalization Analysis in Poultry: Systematic Review of Health, Behavior, and Welfare Monitoring by Venkatraman Manikandan, Suresh Neethirajan

“…Advanced applications spanning emotion recognition, disease detection, and behavioral phenotyping demonstrate unprecedented potential for real-time welfare assessment. …”
Get full text

An adaptive continuous threshold wavelet denoising method for LiDAR echo signal by Dezhi Zheng, Tianchi Qu, Chun Hu, Shijia Lu, Zhongxiang Li, Guanyu Yang, Xiaojun Yang

“…In this paper, a novel adaptive continuous threshold wavelet denoising algorithm is proposed to filter out the noise. The algorithm features an adaptive threshold and a continuous threshold function. …”
Get full text

THE CURRENT STATE OF ARTIFICIAL INTELLIGENCE IN RADIOLOGY – A REVIEW OF THE BASIC CONCEPTS, APPLICATIONS, AND CHALLENGES by Mariana Yordanova

“…CNNs excel in tasks like lesion detection and disease classification, aiding radiologists in diagnosing conditions more accurately. …”
Get full text

Explainable Machine Learning Models for Colorectal Cancer Prediction Using Clinical Laboratory Data by Rui Li MS, Xiaoyan Hao MS, Yanjun Diao MD, Liu Yang MS, Jiayun Liu MD

“…Incorporating stool miR-92a detection into the model further improved diagnostic performance. …”
Get full text

Artificial intelligence in pancreatic intraductal papillary mucinous neoplasm imaging: A systematic review. by Muhammad Ibtsaam Qadir, Jackson A Baril, Michele T Yip-Schneider, Duane Schonlau, Thi Thanh Thoa Tran, C Max Schmidt, Fiona R Kolbinger

“…Based on the Fukuoka and Kyoto international consensus guidelines, the current clinical management of intraductal papillary mucinous neoplasm (IPMN) largely depends on imaging features. While these criteria are highly sensitive in detecting high-risk IPMN, they lack specificity, resulting in surgical overtreatment. …”
Get full text

Microcephalic osteodysplastic primordial dwarfism type II in four Indian children with PCNT variants by Amit Singh, Mahak Garg, Mohammed Shariq, Preeti Khetarpal, Inusha Panigrahi

“…Data were analyzed using standard bioinformatic algorithms and possible causal variants identified. …”
Get full text

Development of Cyber Security Platform for Experiential Learning by Abhishek Vaish, Ravindra Kumar, Samo Bobek, Simona Sternad

“…The present research proposes a comprehensive web-based platform that can be used to communicate, collaborate and practice various use cases in the domain of network intrusion detection tools using machine learning algorithms and to evaluate user experience. …”
Get full text

Atmospheric Modeling for Wildfire Prediction by Fathima Nuzla Ismail, Brendon J. Woodford, Sherlock A. Licorish

“…Our study focuses on developing wildfire prediction models using one-class classification algorithms. These include Support Vector Machine, Isolation Forest, AutoEncoder, Variational AutoEncoder, Deep Support Vector Data Description, and Adversarially Learned Anomaly Detection. …”
Get full text

Monitoring Moso bamboo (Phyllostachys pubescens) forests damage caused by Pantana phyllostachysae Chao considering phenological differences between on-year and off-year using UAV h... by Anqi He, Zhanghua Xu, Yifan Li, Bin Li, Xuying Huang, Huafeng Zhang, Xiaoyu Guo, Zenglu Li

“…This study utilizes the Recursive Feature Elimination (RFE) algorithm to investigate hyperspectral remote sensing characteristics of P. phyllostachysae in Moso bamboo forests. …”
Get full text

MOTH: Memory-Efficient On-the-Fly Tiling of Histological Image Annotations Using QuPath by Thomas Kauer, Jannik Sehring, Kai Schmid, Marek Bartkuhn, Benedikt Wiebach, Slaven Crnkovic, Grazyna Kwapiszewska, Till Acker, Daniel Amsel

“…With the help of artificial intelligence algorithms, it is now possible to detect certain structures and morphological features on whole slide images automatically. …”
Get full text

Automated Foveal Avascular Zone Segmentation in Optical Coherence Tomography Angiography Across Multiple Eye Diseases Using Knowledge Distillation by Peter Racioppo, Aya Alhasany, Nhuan Vu Pham, Ziyuan Wang, Giulia Corradetti, Gary Mikaelian, Yannis M. Paulus, SriniVas R. Sadda, Zhihong Hu

“…Although several automated FAZ detection and segmentation algorithms have been developed for use with OCTA, their performance can vary significantly due to differences in data accessibility of OCTA in different retinal pathologies, and differences in image quality in different subjects and/or different OCTA devices. …”
Get full text

Wearable Artificial Intelligence for Sleep Disorders: Scoping Review by Sarah Aziz, Amal A M Ali, Hania Aslam, Alaa A Abd-alrazaq, Rawan AlSaad, Mohannad Alajlani, Reham Ahmad, Laila Khalil, Arfan Ahmed, Javaid Sheikh

“…The primary selection criterion was the inclusion of studies that utilized AI algorithms to detect or predict various sleep disorders using data from wearable devices. …”
Get full text

FL-DBENet: Double-branch encoder network based on segment anything model for farmland segmentation of large very-high-resolution optical remote sensing images by W. Feng, F. Guan, C. Sun, W. Xu, W. Xu

“…This paper introduces FL-DBENet, a farmland extraction network that builds on SAM’s strengths. FL-DBENet features a general-specialized double-branch encoder network: the general branch leverages SAM’s robust edge detection to capture precise farmland boundaries, while the specialized branch incorporates the lightweight SegFormer encoder to provide SAM with targeted prompts on farmland features. …”
Get full text

A Picking Point Localization Method for Table Grapes Based on PGSS-YOLOv11s and Morphological Strategies by Jin Lu, Zhongji Cao, Jin Wang, Zhao Wang, Jia Zhao, Minjie Zhang

“…More specifically, the network PGSS-YOLOv11s is composed of an original backbone of the YOLOv11s-seg, a spatial feature aggregation module (SFAM), an adaptive feature fusion module (AFFM), and a detail-enhanced convolutional shared detection head (DE-SCSH). …”
Get full text

Deep Unified Model For Face Recognition Based on Convolution Neural Network and Edge Computing by Muhammad Zeeshan Khan, Saad Harous, Saleet Ul Hassan, Muhammad Usman Ghani Khan, Razi Iqbal, Shahid Mumtaz

“…This paper proposes an algorithm for face detection and recognition based on convolution neural networks (CNN), which outperform the traditional techniques. …”
Get full text

Transient Voltage Unit Protection Based on Fault Information Fusion by Zhenwei Guo, Ruiqiang Zhao, Haojie Li, Yongyan Jiang

“…Transient voltage high-frequency instantaneous amplitudes are obtained and integrated using the Variational Mode Decomposition Hilbert Transform (VMD-HT) to increase the stability of high-frequency fault signal detection. This algorithm solved the original problem of 42.2% cross-data, 41.6% false operations, and 8.9% refusal to operate. …”
Get full text

Next-Gen Video Watermarking with Augmented Payload: Integrating KAZE and DWT for Superior Robustness and High Transparency by Himanshu Agarwal, Shweta Agarwal, Farooq Husain, Rajeev Kumar

“…Utilizing the 2D-DWT along with the KAZE feature detection algorithm, which incorporates the Accelerated Segment Test with Zero Eigenvalue, scrutinize and pinpoint data points that exhibit circular symmetry. …”
Get full text

Artificial intelligence-based automated breast ultrasound radiomics for breast tumor diagnosis and treatment: a narrative review by Yinglin Guo, Ning Li, Chonghui Song, Juan Yang, Yinglan Quan, Hongjiang Zhang

“…In recent years, the integration of artificial intelligence (AI) with radiomics has significantly enhanced the process of analyzing and extracting meaningful features from large and complex radiomic datasets through the application of machine learning (ML) and deep learning (DL) algorithms. …”
Get full text

State of Health Estimation of Li-Ion Battery via Incremental Capacity Analysis and Internal Resistance Identification Based on Kolmogorov–Arnold Networks by Jun Peng, Xuan Zhao, Jian Ma, Dean Meng, Shuhai Jia, Kai Zhang, Chenyan Gu, Wenhao Ding

“…Three commonly used machine learning methods (BP, LSTM, TCN) and two hybrid algorithms (LSTM-KAN and TCN-KAN) were used to establish the SOH estimation model. …”
Get full text